Image processing method and device for processing image, server and storage medium

ABSTRACT

Embodiments of the present provide a method and a device for processing an image, server and storage medium. The method includes: determining, based on an object type of an object in an image to be processed, a feature expression of the object in the image to be processed; and determining an entity associated with the object in the image to be processed based on the feature expression of the object in the image to be processed and a feature expression of an entity in a knowledge graph.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority and benefits to Chinese Application No.201910184113.4, filed on Mar. 12, 2019, the entire content of which isincorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to the field of image processing, andmore particularly, to a method and a device for processing an image, aserver and a storage medium.

BACKGROUND

With rapid increasing of image and video data on the Internet, thedemand for image processing technology increases. The image processingtechnology may be used to quickly understand content of pictures orvideos, to labeling pictures or videos with corresponding tags, and torecommend certain kinds of pictures or videos to users.

SUMMARY

Embodiments of the present disclosure provide a method for processing animage. The method includes: determining, based on an object type of anobject in an image to be processed, a feature expression of the objectin image to be processed; and determining an entity associated with theobject in the image to be processed based on the feature expression ofobject in the image to be processed and a feature expression of anentity in a knowledge graph.

Embodiments of the present disclosure provide a server. The serverincludes: one or more processors and a memory configured to store one ormore programs. When the one or more programs are executed by the one ormore processors, the one or more processors are caused to implement themethod for processing an image according to above embodiments of thefirst aspect.

Embodiments of the present disclosure provide a computer readablestorage medium having computer programs stored thereon. When theprograms are executed by a processor, the method for processing an imageaccording to above embodiments of the present disclosure is implemented.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart illustrating a method for processing an imageaccording to embodiments of the present disclosure.

FIG. 2 is a flowchart illustrating a method for processing an imageaccording to embodiments of the present disclosure.

FIG. 3 is a schematic diagram of determining an entity associated to animage according to embodiments of the present disclosure.

FIG. 4 is a schematic diagram illustrating a device for processing animage according to embodiments of the present disclosure.

FIG. 5 is a schematic diagram illustrating a server according toembodiments of the present disclosure.

DETAILED DESCRIPTION

The present disclosure will be described in detail below with referenceto the accompanying drawings and the embodiments. It should beunderstood that, the specific embodiments described herein are only usedto explain the present disclosure rather than to limit the presentdisclosure. In addition, it should also be noted that, for convenienceof description, only part but not all structures related to the presentdisclosure are illustrated in the accompanying drawings.

Currently, a method of labelling an image based on an image processingtechnology to obtain a result of labelling is mainly to set an objecttag and to analyze related actions. However, this method may onlyanalyze the content shallowly, and cannot analyze the content deeply toobtain a deep result. As a result, the result of labelling may benon-semantic, which leads to an inaccurate analysis result. In addition,a method of labelling an image using external knowledge graph data toobtain the result of labelling improves the identification effect oftags. However, the tag may be non-semantic, and the picture contentcannot be deeply understood.

Therefore, embodiments of the present disclosure provide a method and adevice for processing an image, a server and a storage medium, to deeplyunderstand the image using a knowledge graph and to obtain a structuredsemantic tag of the image.

With embodiments of the present disclosure, based on the object type ofthe object in the image to be processed, the feature expression of theobject in the image to be processed is determined, the entity associatedwith the object in the image to be processed is determined based on thefeature expression of object in the image to be processed and thefeature expression of the entity in the knowledge graph to enable deepunderstanding of the image, thereby facilitating expansion andverification of the entity associated with the object and realizingdetermination of a structured semantic tag.

FIG. 1 is a flowchart illustrating a method for processing an imageaccording to embodiments of the present disclosure. The method accordingto embodiments may be applicable for deeply understanding an image tofurther determine an image tag. The method may be implemented by adevice for processing an image. The device may be implemented bysoftware and/or hardware, and the device may be integrated in a server.As illustrated in FIG. 1, the method according to embodiments mayinclude the following.

At block 110, a feature expression of an object in an image to beprocessed is determined based on an object type of the object in theimage to be processed.

In detail, the image to be processed may be a picture or an image from avideo. Features of the object may include entities in the image to beprocessed. In an example, determining, based on the object type of theobject in the image to be processed, the feature expression of theobject in the image to be processed may include determining the featureexpression of the object in the image to be processed based on theobject type and an object image area of the object included in the imageto be processed. Exemplarily, target identification may be performed onthe image to be processed and multiple objects in the image to beprocessed may be identified, classified and delineated, to obtain theobject type and the object image area of each object. Pixel data of theimage to be processed may be input into an identification model toobtain the object type of each object. Further, the pixel data may beinput into a depth learning model corresponding to each object type toperform feature extraction on the image to be processed and to obtainthe feature expression. In an example, the pixel data may be input intoa convolutional neural network (CNN) model for feature extraction toobtain the feature expression corresponding to each object. In someexamples, the object of each object type is input to a deep learningmodel corresponding to the object type to extract features to obtain thefeature expression of the object. For example, an image of a human facetype is input into a face identification model, such as DeepID, DeepFaceor FaceNet to perform feature extraction on the human face to obtain thefeature expression of the human face.

At block 120, an entity associated with the object in the image to beprocessed is determined based on the feature expression of the object inthe image to be processed and the feature expression of the entity inthe knowledge graph.

In detail, the feature expression of the object may be object featuresdisplayed in the image to be processed, such as shape, color, andtexture of the object. The feature expression of the object in the imageto be processed is matched with feature expressions in the knowledgegraph, and an entity in the knowledge graph matching with the featureexpression of the object in the image to be processed may be determinedas the entity associated with the object in the image to be processed.In an example, determining the entity associated with the object in theimage to be processed based on the feature expression of the object inthe image to be processed and the feature expression of the entity inthe knowledge graph may include: determining the entity associated withthe object in the image to be processed based on the feature expressionof the object in the image to be processed, a feature expression of theimage to be processed, and a feature expression of text associated withthe image to be processed, the feature expression of the entity in theknowledge graph and entity attribute information. Exemplarily, inpractical applications, an image provided on the Internet usually hastext associated with the images, for example, text informationassociated with the image to be processed, such as text content, asource title, and an article title corresponding to the image. Thefeature expression of the text is obtained. The entity is furtherdetermined by combining the feature expression of the text. The entityattribute information of the entity in the knowledge graph includes anessential attribute of the object, for example, attribute information ofa person in the image to be processed such as age, gender, andoccupation. The feature expression of the object in the image to beprocessed, the feature expression of the image to be processed, thefeature expression of the text associated with the image to be processedare matched with the feature expression of the entity in the knowledgegraph and the entity attribute information and feature expressions arecompare, to determine the entity associated with the object in the imageto be processed.

In an example, after the entity associated with the object in the imageto be processed is determined based on the feature expression of theobject in the image to be processed and the feature expression of theentity in the knowledge graph, the method further includes: determininga feature expression of an article based on an entity in the article towhich the image to be processed belongs and determining a relevancebetween the article and the image based on the feature expression of thearticle and the entity associated with the object in the image to beprocessed. In detail, it is possible that a low relevance exists betweenthe article to which the image to be processed belongs and the image tobe processed. It is likely that the image and the article are linked dueto identification errors of the image or the article. Therefore, it maybe determined whether the image to be processed is relevant to thearticle based on the relevance. Exemplarily, the feature expression ofthe article in the image to be processed is determined, and therelevance between the article and the image to be processed is furtherdetermined based on the feature expression of the article and the entityassociated with the object in the image to be processed.

With the technical solution according to embodiments of the presentdisclosure, the feature expression of the object in an image to beprocessed is determined based on the object type of the object in theimage to be processed, and the entity associated with the object in theimage to be processed is determined based on the feature expression ofthe object in the image to be processed and the feature expression ofthe entity in the knowledge graph. Determining the entity associatedwith the object in the image to be processed based on the featureexpression of the object and the feature expression of the entity in theknowledge graph enables deep understanding of the image, therebyfacilitating expansion and verification of the entity associated withthe object and realizing determination of a structured semantic tag.

FIG. 2 is a flowchart illustrating a method for processing an imageaccording to embodiments of the present disclosure.

This embodiment is optimized on the basis of embodiment 1, and detailswhich are not described in this embodiment can be referred to the aboveembodiment. As illustrated in FIG. 2, the method according to thisembodiment may include the following.

At block 210, a feature expression of an object in an image to beprocessed is determined based on an object type of the object in theimage to be processed.

At block 220, an entity associated with the object in the image to beprocessed is determined based on the feature expression of the object inthe image to be processed and a feature expression of an entity in aknowledge graph.

At block 230, a first determination manner is determined as adetermination manner of the entity determined.

In detail, the first determination manner may be the manner in the block220 for determining the entity. That is, the entity associated with theobject in the image to be processed is determined based on the featureexpression of the object in the image to be processed and the featureexpression of the entity in the knowledge graph.

At block 240, the entity associated with the object in the image to beprocessed is determined by at least one secondary determination manner.

In detail, the secondary determination manner may be a determinationmanner other than the determination manner of the block 220. Forexample, an image provided on the Internet usually has text associatedwith the image, such as, text information like text content, a sourcetitle, and an article title corresponding to the image. Text entitiescan be obtained through a feature expression of the text. However, arelevance between the text and the image may be low. Therefore, a mannerof determining the associated entity based on the text informationrelevant to the image to be processed, with a secondary method ofdetermining an image relevance result based on the text information, maybe determined as a secondary determination manner.

In an example, determining the entity associated with the object in theimage to be processed based on at least one secondary determinationmanner may include matching the image to be processed with an image of acandidate entity to determine the entity associated with the image to beprocessed. In an example, determining the entity associated with theobject in the image to be processed based on at least one secondarydetermination manner may include matching the text to which the image tobe processed belongs with the knowledge graph to determine the entityassociated with the image to be processed. Exemplarily, the image to beprocessed is matched with the image of the candidate entity. When amatching result satisfies conditions, it may be determined that thecandidate entity is associated with the image to be processed. In someexamples, the text of the article to which the image to be processedbelongs is matched with the knowledge graph. When a matching resultsatisfies conditions, it may be determined that a matched entity is theentity associated with the image to be processed.

For example, the image to be processed is matched with the image of thecandidate entity to determine an actor entity in the image to beprocessed. In another example, the text of the article to which theimage belongs is matched with the knowledge graph to determine the actorentity associated with the text entity in the article.

At block 250, the entity associated with the object in the image to beprocessed is re-determined based on the determination manner anddetermination frequency of each entity.

For example, the determination manner and the determination frequencycorresponding to the determination manner may be added to the featureexpression of the entity for determining the entity associated with theobject in the image to be processed, such that the entity associatedwith the object in the image to be processed is re-determined. Forexample, based on the re-determined actor entity, a character entity isdetermined from the knowledge graph. The character entity may be addedto the entity associated with the object in the image to be processed.

At block 260, new entities having edge relations with the entityassociated with the object are selected from the knowledge graph.

In detail, the entity associated with the object is verified based onthe new entities having the edge relations with the entity associatedwith the object in the knowledge graph. For example, an entity in theknowledge graph is a TV series named “To the Sky Kingdom”, the entityhaving the edge relation may be: Bai Qian (a character). That is, thecharacter of Bai Qian has the edge relation with the TV series named “Tothe Sky Kingdom”. By selecting the new entities having the edgerelations with the entity associated with the object, attributes of theentity may be deeply understood.

At block 270, an updated entity associated with the image is selectedfrom the new entities based on a relation among the new entities.

In detail, the relation among the new entities is obtained. For example,an intersection operation is performed on the new entities. Theintersection of the new entities may be used as the updated entityassociated with the image. For example, if three entities are associatedwith one image, three sets of new entities having the edge relation withthe three entities respectively are selected from the knowledge graph.The intersection of the three sets of new entities corresponding t thethree entities are used as the updated entity associated with the image.Exemplarily, the TV series that each actor entity in the image hasstarred in are obtained. The intersection of the TV series are obtainedas the updated entity associated with the image.

In an example, FIG. 3 is a schematic diagram of determining an entityassociated with an image according to Embodiment 2 of the presentdisclosure. As illustrated in FIG. 3, an entity associated with anobject in an image to be processed is determined. An entitycorresponding to the object in the image is obtained by imageidentification and classification, and feature extraction. For example,entities in the image are identified as actors, namely Liu Bei, Guan Yu,and Zhang Fei. Based on entities in the knowledge graph that have theedge relation with Liu Bei, Guan Yu, and Zhang Fei, it may be determinedthat each character corresponds to the TV series named “Romance of theThree Kingdoms”. Further, it may be determined that plot correspondingto the image is Peach Garden Oath, based on the TV series and the threecharacters. In an example, the entities corresponding to the object inthe image are obtained by image identification and classification, andfeature extraction. For example, entities in the image are identified asactors, namely Liu Bei, Guan Yu, and Zhang Fei. A worship action isidentified from the image to be processed. It may be determined that theplot corresponding to the image to be processed is Peach Garden Oath. Itshould be noted that in FIG. 3, in order to distinguish the objectsdisplayed from the background, a white rectangle is used to indicate theobject image area, which is not specifically limited.

In embodiments of the present disclosure, the first determination manneris determined as the determination mode of determining the entity; theentity associated with the object in the image to be processed isdetermined by the at least one secondary determination manner; and theentity associated with the object in the image to be processed isre-determined based on the determination manner and determinationfrequency of each entity. The new entities having the edge relation withthe entity associated with the object are selected from the knowledgegraph. The updated entity associated with the image is selected from thenew entities based on the relations among the new entities. Through thesecondary determination manner, the image may be deeply understood andthe entity associated with the image may be expanded. The updated entityassociated with the image is determined based on the relation among thenew entities, thereby implementing deep analysis and verification of theimage and accurately obtaining a semantic tag of the image.

FIG. 4 is a schematic diagram illustrating a device for processing animage according to embodiments of the present disclosure. The device isapplicable for deeply understanding images to further determine an imagetag. The device may be implemented by software and/or hardware, and maybe integrated in a server. As illustrated in FIG. 4, the device mayfurther include: a feature expression determination module 310, and anassociated entity determination module 320.

The feature expression determination module 310 may be configured todetermine, based on an object type of an object in an image to beprocessed, a feature expression of the object in the image to beprocessed.

The associated entity determination module 320 may be configured todetermine an entity associated with the object in the image to beprocessed based on the feature expression of the object in the image tobe processed and a feature expression of an entity in a knowledge graph.

In an example, the feature expression determination module 310 may beconfigured to determine the feature expression of the object in theimage to be processed, based on the object type of the object in theimage to be processed and an object image area.

In an example, the associated entity determination module 320 may beconfigured to determine the entity associated with the object in theimage to be processed based on the feature expression of the object inthe image to be processed, a feature expression of the image to beprocessed, a feature expression of text associated with the image to beprocessed, the feature expression of the entity in the knowledge graphand entity attribute information.

In an example, the device may further include: an article featureexpression determination module, and a relevance determination module.

The article feature expression determination module may be configured todetermine a feature expression of an article based on an entity in thearticle to which the image to be processed belongs.

The relevance determination module may be configured to determine arelevance between the article and the image based on the featureexpression of the article and the entity associated with the object inthe image to be processed.

In an example, the device may further include: a determination mannerdetermining module, a secondary determination module, and an associatedentity redetermination module.

The determination manner determining module may be configured todetermine a first determination manner as a determination manner ofdetermining the entity.

The secondary determination module may be configured to determine theentity associated with the object in the image to be processed by atleast one secondary determination manner.

The associated entity redetermining module may be configured tore-determine the entity associated with the object in the image to beprocessed based on the determination manner and determination frequencyof each entity.

In an example, the secondary determination module may be furtherconfigured to match the image to be processed with an image of acandidate entity to determine the entity associated with the image to beprocessed; and/or, match a text to which the image to be processedbelongs with the knowledge graph to determine the entity associated withthe image to be processed.

In an example, the device may further include a selecting module and anupdated entity selecting module.

The selecting module is configured to select new entities having edgerelations with the entity associated with the object from the knowledgegraph.

The updated entity selecting module may be configured to select anupdated entity associated with the image from the new entities based ona relation among the new entities.

The device for processing an image according to above embodiments isused to perform the method for processing an image according to any ofthe above embodiments, and the technical principle and the generatedtechnical effect are similar, which are not described herein again.

FIG. 5 is a schematic diagram illustrating a server according toembodiments of the present disclosure. FIG. 5 illustrates a blockdiagram of an exemplary server 412 applicable for implementingembodiments of the present disclosure. The server 412 illustrated inFIG. 5 is merely an example and should not impose any limitation on thefunction and scope of use of the embodiments of the present disclosure.

As illustrated in FIG. 5, the computer device 412 may be represented viaa general computer device form. Components of the computer device 412may include but be not limited to one or more processors or processingunits 416, a system memory 428, a bus 418 connecting various systemcomponents including the system memory 428 and the processing units 416.

The bus 418 represents one or more of several types of bus structures,including a memory bus or a memory controller, a peripheral bus, agraphics acceleration port, a processor, or a local bus using any of avariety of bus structures. For example, these architectures include, butare not limited to, an Industry Standard Architecture (hereinafterreferred to as ISA) bus, a Micro Channel Architecture (hereinafterreferred to as MAC) bus, an enhanced ISA bus, a Video ElectronicsStandards Association (hereinafter referred to as VESA) local bus andPeripheral Component Interconnection (PCI) bus.

The computer device 412 typically includes a variety of computer systemreadable media. These media may be any available media accessible by thecomputer device 412 and includes both volatile and non-volatile media,removable and non-removable media.

The system memory 428 may include a computer system readable medium inthe form of volatile memory, such as a random access memory (hereinafterreferred to as RAM) 430 and/or a high speed cache memory 432. Thecomputer device 412 may further include other removable ornon-removable, volatile or non-volatile computer system storage media.By way of example only, the storage system 434 may be configured to readand write a non-removable and non-volatile magnetic media (not shown inFIG. 5, commonly referred to as a “hard drive”). Although not shown inFIG. 5, a magnetic disk driver for reading from and writing to aremovable and non-volatile magnetic disk (such as “floppy disk”) and adisk driver for a removable and non-volatile optical disk (such ascompact disk read only memory (hereinafter referred to as CD-ROM),Digital Video Disc Read Only Memory (hereinafter referred to as DVD-ROM)or other optical media) may be provided. In these cases, each driver maybe connected to the bus 418 via one or more data medium interfaces. Thememory 428 may include at least one program product. The program producthas a set (such as, at least one) of program modules configured toperform the functions of various embodiments of the present disclosure.

A program/utility 440 having a set (at least one) of the program modules442 may be stored in, for example, the memory 428. The program modules442 include but are not limited to, an operating system, one or moreapplication programs, other programs modules, and program data. Each ofthese examples, or some combination thereof, may include animplementation of a network environment. The program modules 442generally perform the functions and/or methods in the embodimentsdescribed herein.

The computer device 412 may also communicate with one or more externaldevices 414 (such as, a keyboard, a pointing device, a display 424,etc.). Furthermore, the computer device 412 may also communicate withone or more communication devices enabling a user to interact with thecomputer device 412 and/or other devices (such as a network card, modem,etc.) enabling the computer device 412 to communicate with one or morecomputer devices. This communication can be performed via theinput/output (I/O) interface 422. Also, the computer device 412 maycommunicate with one or more networks (such as a local area network(hereafter referred to as LAN), a wide area network (hereafter referredto as WAN) and/or a public network such as an Internet) through anetwork adapter 420. As shown, the network adapter 420 communicates withother modules of the computer device 412 over the bus 418. It should beunderstood that, although not shown, other hardware and/or softwaremodules may be used in connection with the computer device 412. Thehardware and/or software includes, but is not limited to, microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tap Drive and data backup storage system.

The processing unit 416 is configured to execute various functionalapplications and data processing by running programs stored in thesystem memory 428, for example, implementing the method for processingan image according to embodiments of the present disclosure.

Embodiments of the present disclosure provide a computer readablestorage medium having computer programs stored thereon. When theprograms are executed by a processor, the method for processing an imagemay be implemented.

The computer storage medium according to the embodiments of the presentdisclosure may adopt any combination of one or more computer-readablemedium. A computer readable medium may be a computer readable signalmedium or a computer readable storage medium. The computer readablestorage medium may be, but is not limited to, for example, anelectrical, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus, device, component or any combinationthereof. A specific example of the computer readable storage mediainclude (a non-exhaustive list): an electrical connection having one ormore wires, a portable computer disk, a hard disk, a random accessmemory (RAM), a read only memory (ROM), an Erasable Programmable ReadOnly Memory (EPROM) or a flash memory, an optical fiber, a compact discread-only memory (CD-ROM), an optical memory component, a magneticmemory component, or any suitable combination thereof. In context, thecomputer readable storage medium may be any tangible medium including orstoring programs. The programs may be used by an instruction executedsystem, apparatus or device, or a combination thereof.

The computer readable signal medium may include a data signalpropagating in baseband or as part of a carrier which carries computerreadable program codes. Such propagated data signal may be in manyforms, including but not limited to an electromagnetic signal, anoptical signal, or any suitable combination thereof. The computerreadable signal medium may also be any computer readable medium otherthan the computer readable storage medium, which may send, propagate, ortransport programs used by an instruction executed system, apparatus ordevice, or a connection thereof.

The program code stored on the computer readable medium may betransmitted using any appropriate medium, including but not limited towireless, wireline, optical fiber cable, RF, or any suitable combinationthereof.

The computer program code for carrying out operations of embodiments ofthe present disclosure may be written in one or more programminglanguages. The programming language includes an object orientedprogramming language, such as Java, Smalltalk, C ++, as well asconventional procedural programming language, such as “C” language orsimilar programming language. The program code may be executed entirelyon a user's computer, partly on the user's computer, as a separatesoftware package, partly on the user's computer, partly on a remotecomputer, or entirely on the remote computer or server. In a case of theremote computer, the remote computer may be connected to the user'scomputer or an external computer (such as using an Internet serviceprovider to connect over the Internet) through any kind of network,including a Local Area Network (hereafter referred as to LAN) or a WideArea Network (hereafter referred as to WAN).

It should be noted that, the above are only preferred embodiments andapplied technical principles of the present disclosure. Those skilled inthe art should understand that, the present disclosure is not limited tothe specific embodiments described herein, and various obvious changes,readjustments and substitutions that are made by those skilled in theart will not depart from the scope of the present disclosure. Therefore,although the present disclosure has been described in detail by theabove embodiments, the present disclosure is not limited to the aboveembodiments, and more other equivalent embodiments may be includedwithout departing from the concept of the present disclosure, and thescope of the present disclosure is determined by the scope of theappended claims.

What is claimed is:
 1. A method for processing an image, comprising:obtaining an object type of an object contained in an image to beprocessed by classifying the object and obtaining an object image areawhere the object is located; determining a feature expression of theobject by inputting pixel data within the object image area into a deeplearning model corresponding to the object type; and determining anentity associated with the object by performing matching calculationbetween the feature expression of the object and a feature expression ofthe entity in a knowledge graph, the feature expression of the objectand the feature expression of the entity associated with the objectmatching with each other; wherein the method further comprises:obtaining an article to which the image to be processed belongs;determining a feature expression of the article based on an articleentity in the article; and determining a relevance between the articleand the image by performing a matching calculation between the featureexpression of the article and the entity in the knowledge graph.
 2. Themethod of claim 1, wherein determining the entity associated with theobject in the image to be processed based on the feature expression ofthe object in the image to be processed and the feature expression ofthe entity in the knowledge graph comprises: determining the entityassociated with the object in the image to be processed based on thefeature expression of the object in the image to be processed, a featureexpression of the image to be processed, and a feature expression oftext associated with the image to be processed, the feature expressionof the entity in the knowledge graph and entity attribute information,wherein the entity attribute information comprises an essentialattribute of the entity.
 3. The method of claim 1, further comprising:determining a first determination manner as a determination manner ofdetermining the entity; determining the entity associated with theobject in the image to be processed by at least one secondarydetermination manner; and re-determining the entity associated with theobject in the image to be processed based on a determination manner anddetermination frequency of each entity contained in the knowledge graph;the determination manner comprising a first determination manner and atleast one secondary determination manner.
 4. The method of claim 3,wherein determining the entity associated with the object in the imageto be processed by the at least one secondary determination mannercomprises: matching the image to be processed with an image of acandidate entity to determine the entity associated with the image to beprocessed; and/or, matching text to which the image to be processedbelongs with the knowledge graph to determine the entity associated withthe image to be processed.
 5. The method of claim 1, further comprising:selecting new entities having edge relations with the entity associatedwith the object from the knowledge graph, wherein edge relation refersto an edge connecting a new entity with the entity associated with theobject; and selecting an updated entity associated with the image fromthe new entities based on an intersection of the new entities.
 6. Aserver, comprising: one or more processors; a memory, configured tostore one or more programs; and wherein when the one or more programsare executed by the one or more processors, the one or more processorsare caused to: obtain an object type of an object contained in an imageto be processed by classifying the object, obtain an object image areawhere the object is located, and determine a feature expression of theobject by inputting pixel area within the object image area into a deeplearning model corresponding to the object type; and determine an entityassociated with the object by performing matching calculation betweenthe feature expression of the object and a feature expression of theentity in a knowledge graph, the feature expression of the object andthe feature expression of the entity associated with the object matchingwith each other; wherein the one or more processors are caused furtherto: obtain an article to which the image to be processed belongs;determine a feature expression of an article based on an article entityin the article; and determine a relevance between the article and theimage by performing the matching calculation between the featureexpression of the article and the entity in the knowledge graph.
 7. Theserver of claim 6, wherein the one or more processors are caused todetermine the entity associated with the object in the image to beprocessed based on the feature expression of the object in the image tobe processed and the feature expression of the entity in the knowledgegraph by: determining the entity associated with the object in the imageto be processed based on the feature expression of the object in theimage to be processed, a feature expression of the image to beprocessed, and a feature expression of text associated with the image tobe processed, the feature expression of the entity in the knowledgegraph and entity attribute information, wherein the entity attributeinformation comprises an essential attribute of the entity.
 8. Theserver of claim 6, wherein the one or more processors are caused furtherto: determine a first determination manner as a determination manner ofdetermining the entity; determine the entity associated with the objectin the image to be processed by at least one secondary determinationmanner; and re-determine the entity associated with the object in theimage to be processed based on a determination manner and determinationfrequency of each entity contained in the knowledge graph; thedetermination manner comprising a first determination manner and atleast one secondary determination manner.
 9. The server of claim 8,wherein the one or more processors are caused to determine the entityassociated with the object in the image to be processed by the at leastone secondary determination manner by: matching the image to beprocessed with an image of a candidate entity to determine the entityassociated with the image to be processed; and/or, matching text towhich the image to be processed belongs with the knowledge graph todetermine the entity associated with the image to be processed.
 10. Theserver of claim 6, wherein the one or more processors are caused furtherto: select new entities having edge relations with the entity associatedwith the object from the knowledge graph, wherein edge relation refersto an edge connecting a new entity with the entity associated with theobject; and select an updated entity associated with the image from thenew entities based on an intersection of the new entities.
 11. Anon-transitory computer readable storage medium, having computerprograms stored thereon, wherein when the programs are executed by aprocessor, a method for processing an image is implemented, the methodcomprising: obtaining an object type of an object contained in an imageto be processed by classifying the object and obtaining an object imagearea where the object is located; and determining a feature expressionof the object by inputting pixel area within the object image area intoa deep learning model corresponding to the object type; and determiningan entity associated with the object by performing matching calculationbetween the feature expression of the object and a feature expression ofthe entity in a knowledge graph, the feature expression of the objectand the feature expression of the entity associated with the objectmatching with each other; wherein the method further comprises:obtaining an article to which the image to be processed belongs;determining a feature expression of the article based on an articleentity in the article; and determining a relevance between the articleand the image by performing a matching calculation between the featureexpression of the article and the entity in the knowledge graph.
 12. Thenon-transitory computer readable storage medium of claim 11, whereindetermining the entity associated with the object in the image to beprocessed based on the feature expression of the object in the image tobe processed and the feature expression of the entity in the knowledgegraph comprises: determining the entity associated with the object inthe image to be processed based on the feature expression of the objectin the image to be processed, a feature expression of the image to beprocessed, and a feature expression of text associated with the image tobe processed, the feature expression of the entity in the knowledgegraph and entity attribute information, wherein the entity attributeinformation comprises an essential attribute of the entity.
 13. Thenon-transitory computer readable storage medium of claim 11, wherein themethod further comprises: determining a first determination manner as adetermination manner of determining the entity; determining the entityassociated with the object in the image to be processed by at least onesecondary determination manner; and re-determining the entity associatedwith the object in the image to be processed based on a determinationmanner and determination frequency of each entity contained in theknowledge graph; the determination manner comprising a firstdetermination manner and at least one secondary determination manner.14. The non-transitory computer readable storage medium of claim 11,wherein the method further comprising: selecting new entities havingedge relations with the entity associated with the object from theknowledge graph, wherein edge relation refers to an edge connecting anew entity with the entity associated with the object; and selecting anupdated entity associated with the image from the new entities based onan intersection of the new entities.